ArticlePDF Available

Abstract and Figures

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by a wide spectrum of deficits in social interaction, communication, and behavior. There is a significant genetic component to ASD, yet no single gene variant accounts for >1% of incidence. Posttranscriptional mechanisms such as microRNAs (miRNAs) regulate gene expression without altering the genetic code. They are abundant in the developing brain and are dysregulated in children with ASD. Patterns of miRNA expression are altered in the brain, blood, saliva, and olfactory precursor cells of ASD subjects. The ability of miRNAs to regulate broad molecular pathways in response to environmental stimuli makes them an intriguing player in ASD, a disorder characterized by genetic predisposition with ill-defined environmental triggers. In addition, the availability and extracellular stability of miRNAs make them an ideal candidate for biomarker discovery. Here, we discuss 27 miRNAs with overlap across ASD studies, including 3 miRNAs identified in 3 or more studies (miR-23a, miR-146a, and miR-106b). Together, these 27 miRNAs have 1245 high-confidence mRNA targets, a significant number of which are expressed in the brain. Furthermore, these mRNA targets demonstrate over-representation of autism-related genes with enrichment of neurotrophic signaling molecules. Brain-derived neurotrophic factor, a molecule involved in hippocampal neurogenesis and altered in ASD, is targeted by 6 of the 27 miRNAs of interest. This neurotrophic pathway represents one intriguing mechanism by which perturbations in miRNA signaling might influence central nervous system development in children with ASD.
Content may be subject to copyright.
November 2016 | Volume 7 | Article 1761
published: 04 November 2016
doi: 10.3389/fpsyt.2016.00176
Frontiers in Psychiatry |
Edited by:
Mark Nicholas Ziats,
National Institute of Child Health and
Human Development, USA
Reviewed by:
Antonio Benítez-Burraco,
University of Huelva, Spain
Kimberly Raab-Graham,
Wake Forest School
of Medicine, USA
Steven D. Hicks
Specialty section:
This article was submitted
toBehavioral and
a section of the journal
Frontiers in Psychiatry
Received: 15August2016
Accepted: 11October2016
Published: 04November2016
HicksSD and MiddletonFA (2016)
AComparative Review of
microRNAExpression Patterns
inAutism Spectrum Disorder.
Front. Psychiatry 7:176.
doi: 10.3389/fpsyt.2016.00176
A Comparative Review of microRNA
Expression Patterns in Autism
Spectrum Disorder
Steven D. Hicks1* and Frank A. Middleton2,3,4
1 Department of Pediatrics, Penn State College of Medicine, Hershey, PA, USA, 2 Department of Neuroscience and
Physiology, SUNY Upstate Medical University, Syracuse, NY, USA, 3 Department of Psychiatry and Behavioral Sciences,
SUNY Upstate Medical University, Syracuse, NY, USA, 4 Department of Biochemistry and Molecular Biology, SUNY Upstate
Medical University, Syracuse, NY, USA
Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by a
wide spectrum of decits in social interaction, communication, and behavior. There is a
signicant genetic component to ASD, yet no single gene variant accounts for >1% of
incidence. Posttranscriptional mechanisms such as microRNAs (miRNAs) regulate gene
expression without altering the genetic code. They are abundant in the developing brain
and are dysregulated in children with ASD. Patterns of miRNA expression are altered in the
brain, blood, saliva, and olfactory precursor cells of ASD subjects. The ability of miRNAs to
regulate broad molecular pathways in response to environmental stimuli makes them an
intriguing player in ASD, a disorder characterized by genetic predisposition with ill-dened
environmental triggers. In addition, the availability and extracellular stability of miRNAs make
them an ideal candidate for biomarker discovery. Here, we discuss 27 miRNAs with overlap
across ASD studies, including 3 miRNAs identied in 3 or more studies (miR-23a, miR-146a,
and miR-106b). Together, these 27 miRNAs have 1245 high-condence mRNA targets, a
signicant number of which are expressed in the brain. Furthermore, these mRNA targets
demonstrate over-representation of autism-related genes with enrichment of neurotrophic
signaling molecules. Brain-derived neurotrophic factor, a molecule involved in hippocampal
neurogenesis and altered in ASD, is targeted by 6 of the 27 miRNAs of interest. This neu-
rotrophic pathway represents one intriguing mechanism by which perturbations in miRNA
signaling might inuence central nervous system development in children with ASD.
Keywords: autism, microRNA, neurodevelopment, biomarker
In 1993, the rst non-coding antisense RNA sequence was described in Caenorhabditis elegans
(1,2) and termed microRNA (miRNA). Over the next 10years, the roles of miRNAs in modifying
mRNA translation and their potential involvement in human diseases were revealed (3, 4). We
now know that miRNAs play an important role in central nervous system (CNS) development
and function (5, 6) and that dysregulation of miRNAs is tied to alterations in behavior and
Abbreviations: ADI-R, autism diagnostic interview – revised; ADOS, Autism Diagnostic Observation Schedule; ASD, autism
spectrum disorder; AUC, area under the curve; BBB, blood–brain barrier; CNS, central nervous system; CNV, copy number
variant; DAVID, Database for Annotation, Visualization, and Integrated Discovery; HDL, high-density lipoprotein; MeCP2,
methyl CpG-binding protein; miRNA, microRNA; PPI, protein–protein interaction; SFARI, Simons Foundation Autism
Research Initiative; SNP, single nucleotide polymorphism.
Hicks and Middleton miRNA Biomarkers in Autism
Frontiers in Psychiatry | November 2016 | Volume 7 | Article 176
cognition seen in a number of neuropsychiatric disorders (7).
Here, we focus on miRNAs identied in studies of humans
with autism spectrum disorder(ASD).
Brain miRNA expression was rst examined in postmortem
cerebellum from ASD subjects (8). A succession of ensuing
studies reported widespread miRNA dysregulation in the CNS
and periphery, including lymphoblasts (911), blood (1214),
saliva (15), and olfactory precursor cells (16). ese studies have
produced vast amounts of information, some unifying and some
conicting. e way in which reports of circulating miRNAs
instruct our understanding of miRNAs in the CNS is still being
explored (17). To guide that exploration, this review summarizes
current knowledge of miRNA in ASD, with the goal of identify-
ing the most consistent ndings across studies with potential
implications for biomarker discovery.
Clinical Aspects of Autism Spectrum
Autism spectrum disorder is a heterogeneous disorder typied
by decits in social communication and restricted, repetitive
patterns of behavior (18). Children with ASD are oen adher-
ent to daily routines, preoccupied with specic topics, and over
sensitive to sensory inputs such as sounds or textures (19). In
most cases, ASD symptoms are rst recognized in early child-
hood. e average age of ASD diagnosis is 4years (20). Rates of
ASD are increasing. Recent reports from the Centers for Disease
Control and Prevention estimate the prevalence of ASD to be
1:68 children (1:45 among males) (21). is increase may be the
result of multiple factors including public awareness, changes
in diagnostic criteria, and environmental inuences. However,
genetics also plays an important role in ASD (22).
Genetic Contributions to ASD
A large number of familial studies demonstrate that ASD is a
heritable disorder (23, 24), with estimated genetic contribu-
tions accounting for >50–60% of ASD risk (25). Indeed, a large
number of genetic variants and chromosomal abnormalities are
linked to ASD (26). ese variants tend to be highly penetrant
but rare. However, in some cases, common variants with low
penetrance have been reported (27). Neither explains the inci-
dence of ASD in the general population. Genetic epidemiologic
studies have also shown that ASD is not a single disease but a
constellation of symptoms involving multiple gene networks
(28). Posttranscriptional mechanisms, such as miRNA, that
broadly inuence gene expression without altering the DNA
code represent one means of altering entire gene networks (29).
Increasingly, investigators have turned toward these mechanisms
to explain the dysregulation of neurodevelopmental pathways
that occurs in ASD (30, 31).
miRNAs Regulate Posttranscriptional
Gene Expression
MicroRNAs are short (18–25 nucleotide), non-coding RNA mol-
ecules (32) that inuence gene expression and numerous cellular
processes, including proliferation, dierentiation, and apoptosis
(33). Over 2500 mature miRNAs (and 1800 precursors) have
been identied in humans (34). Many of these are evolutionarily
conserved (35). e genes for miRNAs are found in inter- or
intra-genic regions (36). ey are transcribed into precursor
hairpin structures (pri-miRNAs) by RNA polymerase-II and
trimmed into pre-miRNA by the RNase Drosha. Aer leaving
the nucleus, Dicer liberates the pre-miRNA into a single-stranded
molecule that can be bound within the miRNA-induced silencing
complex (miRISC) (37). e targeting of the mature miRISC to
individual mRNAs is based on the “seed sequences” that reside at
the 5 end of the miRNA and are complementary to the 3 regions
of mRNAs. Each seed sequence aligns with hundreds of mRNAs,
and there are several miRNAs with shared seed sequences (38).
Once bound to a target mRNA, the miRISC complex reduces the
eciency of gene expression by repressing protein translation or
promoting degradation of the mRNA transcript (37).
miRNAs in the Brain and Periphery
MicroRNAs regulate approximately two-thirds of human
mRNAs (39). Each miRNA has hundreds of potential mRNA
targets, giving it the ability to modulate entire gene networks. In
addition, miRNAs can circulate within exosomes, microvesicles,
or RNA-binding proteins such as high-density lipoproteins, and
thereby travel extracellularly to alter gene expression in distant
tissues. is renders them exceedingly stable and easily measured
in serum, plasma, saliva, urine, and other biouids (40). However,
miRNAs are most ubiquitous in the CNS, which expresses an
estimated 70% of all miRNAs (41). Notably, expression of brain
miRNAs changes throughout childhood and varies across brain
regions (42). Neurons and glia are abundant sources of miRNAs,
which can be readily transported across the blood–brain barrier
(BBB). Moreover, miRNAs in neurons also help compartmental-
ize or localize mRNA expression and translation within specic
subcellular regions such as axons and dendrites.
The Role of miRNAs in Neurodevelopment
MicroRNAs play important roles in neurogenesis, synaptogen-
esis, and neuronal migration (43, 44). A major means by which
these roles are manifest is the eect that neuronal miRNAs have
on spatial localization or compartmentalization of protein trans-
lation in dierent neuronal subregions, such as axons, dendrites,
and synapses (45, 46). Recent data support the relevance of these
processes for neurodevelopmental disorders. For example, in
mice with fragile X-associated tremor/ataxia syndrome (FXTAS),
the fragile X mental retardation (FMR1) transcript is targeted by
miR-221, miR-101, and miR-129-5p (47). Synaptosomal prepara-
tions from FXTAS mice demonstrate dysregulation of transcripts
in learning and social interaction along with reduced levels of
miR-221. Other examples include studies of disruption of miRNA
regulatory genes such as Dicer or DGCR8, which produce altera-
tions in synaptic plasticity within the prefrontal cortex (48). e
gene for DGCR8 is located at 22q11.21 and a well-characterized
hemi-deletion of this region (referred to as velocardiofacial
syndrome) produces a complex phenotype along with ASD traits
in approximately one-third of subjects (49). Moreover, mice with
DGCR8 haploinsuciency have reduced dendrite complex-
ity, fewer neuronal spines, and decreased neurogenesis (50).
Complementing these data, dysregulation of miRNA expression
Hicks and Middleton miRNA Biomarkers in Autism
Frontiers in Psychiatry | November 2016 | Volume 7 | Article 176
has been described in a number of developmental, neuropsychi-
atric, and neurodegenerative disorders including schizophrenia,
Alzheimer’s disease, and Parkinson’s disease (36).
The Role of miRNAs in ASD Pathogenesis
Four studies examining postmortem brain tissue of human
subjects with ASD have identied 91 miRNAs with dierential
expression compared with typically developing controls. e
exact pathophysiologic role of these miRNAs in ASD remains
unclear; however, many are implicated in neurogenesis and syn-
aptogenesis. A study of cerebellar cortex in 13 adults with ASD (8)
identied 28 miRNAs with dierential expression and 7 of these
targeted the SHANK3 transcript. SHANK3 is involved in regula-
tion of synaptic density and has copy number variants (CNVs)
and point mutations in nearly 1% of ASD cases (51). A study by
Wu etal. (52) examined cerebellar cortex in 28 ASD subjects. e
authors found miR-21-3p was upregulated in ASD subjects and
demonstrated that its overexpression in human neuroprogenitor
cells could repress multiple M16 hub genes, including DLGAP1
(a scaold protein that interacts with SHANK3). us, SHANK3
represents one pathophysiologic target with an established role in
ASD that appears to be targeted by multiple miRNAs identied in
multiple studies of ASD brain tissue.
Overlapping miRNAs in ASD
is review synthesizes the ndings of 12 studies, which compared
miRNA expression in human cases of ASD to miRNA proles in
healthy controls (Tab l e  1 ). Four of these studies involved brain
tissue (8, 5254), three involved peripheral blood (1214), one
examined saliva (15), one examined olfactory precursor cells
(16), and three employed cultured lymphoblasts (911).
e 12 studies together identied 219 miRNAs with potential
implications in ASD (Table 2A), including 185 unique and 34
overlapping miRNAs (15.5%). Twenty-seven overlapping miR-
NAs changed in the same direction (Table 2B) in 2 studies,
and 3 changed in the same direction in 3 studies (miR-23a,
miR-146a, and miR-106b). Notably, 14 of the 27 miRNAs shared
seed sequences with other ASD-associated miRNAs, suggesting
that the pathophysiology of miRNA dysregulation in ASD might
be controlled, at least in part, at the seed level. ere is no single
miRNA identied in all the 12 studies. is may be a function of
ASD heterogeneity, miRNA tissue specicity, or advances in RNA
quantication, which have led to varying alignment techniques
and identication of new miRNAs.
Functional Signicance of Overlapping
miRNAs and Their Gene Targets in ASD
Of the 27 overlapping miRNAs, 22 show dierential regulation
in studies of the CNS. e remaining ve are expressed in the
developing brain (42, 53). ese 27 were predicted to target 1245
mRNAs (Table S1 in Supplementary Material) with target scores
93 (55).1 Notably, 86 of these mRNAs overlapped with the 519
human ASD candidate genes with supportive evidence existing
in the Simons Foundation Autism Research Initiative (SFARI)
database,2 representing a 6.6-fold enrichment for ASD-associated
genes compared to chance (odds ratio: 7.7, 95th CI = 6.0–9.9,
z=16.2, p<0.0001).
We next examined the evidence for functional enrichment of
specic biologic processes using the Database for Annotation,
Visualization, and Integrated Discovery (DAVID). is revealed
225 total cluster mappings (56), with the top nodes including
a KEGG pathway involved in neurotrophin signaling [n=25
genes, false discovery rate (FDR)= 0.0018]. In fact, this node
had the largest fold change (FC) (3.0-fold) among all detected
clusters (Table S2 in Supplementary Material). DAVID enrich-
ment analysis also identied 662 (55.9%) of the target mRNAs
as expressed in brain tissue, with additional enrichment of genes
expressed in epithelium (n=268), fetal kidney (n=35), placenta
(n=275), amygdala (n=61), and T-cells (n=37) (Table S3 in
Supplementary Material).
To further examine functional relevance of the most probable
mRNA targets, the Ingenuity Pathway Analysis (IPA) databases
for “Canonical Pathways” and “Diseases and Biological Function
were used. ese analyses were performed using a restricted
set of 293 mRNAs that were predicted to be targets of the 27
overlapping miRNAs with miRDB target scores 95. e results
indicated enrichment of 20 Canonical Pathways (FDR< 0.05),
including two related to neurotrophin signaling (Table S4 in
Supplementary Material). Moreover, the “Diseases and Biological
Function” analysis yielded a list of 500 networks for the 293
mRNA targets, including 93 (18.6%) involved in brain function,
of which 35 survived FDR correction (Table S5 in Supplementary
Material). ese represented several functions, including brain
development, neurogenesis, neural and glial dierentiation,
synaptic development and function, and cognitive and motor
function. e diseases identied among the brain-related
networks included schizophrenia, abnormal brain morphology
and trigeminal nerve morphology, abnormal posture, cognitive
impairment, and seizure disorders.
Upstream Regulatory analysis was performed in IPA consider-
ing only experimentally observed and high-condence miRNA
mRNA interactions, which reduced our list of 27 miRNAs to
20. Notably, 199 mRNAs were targeted by 2 miRNAs, and 55
genes were targeted by 5 of the 20 miRNAs. e signicance of
this was determined using a Fisher’s exact test, which conrmed
enrichment for 49 mRNAs (Table S6 in Supplementary Material).
Protein–protein interaction (PPI) networks were also explored
in the 199 target genes using the STRINGv10 database,3 which
yielded a signicant PPI network containing 137 edges and several
nodes involved in brain function (Figure S1 in Supplementary
Material). Among this network was an intriguing interaction
between neuronal cell adhesion molecule (NRCAM; involved
in directional signaling of axonal cone growth and implicated in
ASD) (57), semaphorin 3a (SEMA3A; a chemoattractive agent
involved in axon guidance) (58), and brain-derived neurotrophic
factor (BDNF; essential for synaptogenesis in the developing
hippocampus and altered in the periphery of ASD subjects) (59).
TABLE 1 | miRNA biomarker study characteristics and ndings.
et al. (8)
et al. (53)
et al. (54)
et al. (52)
et al. (13)
et al. (14)
et al. (12)
et al. (15)
et al. (16)
et al. (10)
et al. (9)
et al. (11)
Biomaterial Postmortem
area 10
areas 22, 41,
and 42
area 9
Serum Peripheral
Saliva Olfactory
mucosal stem
cells + skin
broblasts or
cell lines
Lymphoblast cell
cell lines
# participants 13 ASD, 13
12 ASD, 12
10 ASD, 8
28 ASD, 28
55 ASD, 55
5 ASD/5 control
30 ASD,
25 control
24 ASD,
21 control
8 ASD, 6
14 ASD,
14 sibling
controls (3
6 ASD, 6 controls 20 ASD, 22
sibling controls
Not described Age 30
years, 83%
male, ADI-R
(no scores
PMI 26 h
Age 31 years,
50% male,
some ADI-R,
Autism Tissue
PMI 23 h
Age 31 years,
82% male,
some ADI-R,
17% with 15q
PMI 24 h
Age 11 years,
87% male,
ADI-R (35),
Age 5 years,
80% male,
DSM-4 criteria,
Age 8 years,
80% male,
Age 9 years,
79% male,
ADOS (10.6),
Behavior (71)
Age 30 years,
70% male,
No ages,
100% male,
Age 10 years
old, 50% male,
ADI-R (no scores),
Autism Genetics
No age or
sex, ADOS
(“severe,” but
no scores),
with qRT-PCR
microarray, no
PCR array
with qRT-PCR
with qRT-PCR
Microarray RNA-seq Microarray
with qRT-PCR
with qRT-PCR
miRNA microarray
with qRT-PCR
with qRT-PCR
# miRNAs
377 1104 1733 699 125 2578 Unknown 246 667 1237 150 708
# differentially
28 20 658 14 44 114 443 916
% differentially
7 2 0.3 811 2Unknown 60.6 3 6 2
Criteria for
z-test of
individual ASD
Ct vs. pooled
controls w/FDR
t-test with
FDR < 0.05
p < 0.005, FC
> 1.2
Linear mixed
effects model,
FDR < 0.05
p < 0.05
p < 0.05
p < 0.05,
intensity >3
FDR < 0.15
Whitney p <
(PTM) analysis
p < 0.05 after
Bonferroni, FC
1.5 FC in
# overlapping
with other
29 35 014 14 16 043 125 44 13
in 3 ASD
(4.4 FC,
p = 5.47E81)
(1.24 FC,
p = 0.0012)
(1.5 FC,
FDR 1.5E4)
miR-572 miR-451a
(p = 4.58E5)
(p = 0.03)
miR-628-5p (Z
diff 1.13, FDR
(p < 0.001)
(1.54 FC,
(changed in all 6
ASD individuals)
(1.81 FC,
p = 2.51E05)
ADI-R, Autism Diagnostic Inventory – Revised; ADOS, Autism Diagnostic Observation Schedule; ASD, autism spectrum disorder; Ct, cycles-to-threshold; FC, fold change; FDR, false discovery rate correction; PMI, postmortem
interval; qRT-PCR, quantitative reverse-transcription/real time polymerase chain reaction; RNA-Seq, ribonucleic acid sequencing; sib, sibling.
Hicks and Middleton miRNA Biomarkers in Autism
Frontiers in Psychiatry | November 2016 | Volume 7 | Article 176
Hicks and Middleton miRNA Biomarkers in Autism
Frontiers in Psychiatry | November 2016 | Volume 7 | Article 176
TABLE 2 | Cross-tissue miRNA biomarkers with seed sequences.
A. Brain Serum Saliva Lymphoblast Olfactory SCs
etal. (8)
etal. (53)
etal. (54)
etal. (52)
etal. (13)
etal. (14)
etal. (12)
Hicks etal.
etal. (10)
etal. (9)
etal. (11)
etal. (16)
Up-regulated miR-106a miR-7-5p miR-664-3p miR-10a-5p miR-19b-3p miR-34b-3p miR-7-5p miR-16-2 miR-23a miR-10a miR-146a
miR-106b miR-19a-3p miR-4709-3p miR-18b-5p miR-27a-3p miR-34c-3p miR-28-5p miR-106b miR-23b miR-30a
miR-140 miR-19b-3p miR-4753-5p miR-20b-5p miR-101-3p miR-483-5p miR-127-3p miR-132 miR-132 miR-181a
miR-146b miR-21-3p miR-21-3p miR-106-5p miR-494 miR-140-3p miR-133b miR-146a miR-181b
miR-181d miR-21-5p miR-23a-3p miR-130a-3p miR-564 miR-191-5p miR-136 miR-146b miR-181c
miR-193b miR-142-3p miR-107 miR-195b-5p miR-574-5p miR-218-5p miR-139 miR-663 miR-199b-5p
miR-320a miR-142-5p miR-129-2-3p miR-575 miR-335-3p miR-148b miR-338-3p
miR-381 miR-144-3p miR-130b-5p miR-921 miR-628-5p miR-153 miR-486-3p
miR-432 miR-146a-5p miR-148a-3p miR-1246 miR-2467-5p miR-182 miR-486-5p
miR-539 miR-155-5p miR-155-5p miR-1249 miR-3529-3p miR-189 miR-500
miR-550 miR-219-5p miR-218-2-3p miR-1273c miR-190 miR-502-3p
miR-652 miR-338-5p miR-221-3p miR-4270 miR-199b miR-548
miR-379-5p miR-223-3p miR-4299 miR-211
miR-451a miR-335-3p miR-4436a miR-219
miR-494 miR-363-3p miR-4443 miR-326
miR-3168 miR-424-3p miR-4516 miR-367
miR-424-5p miR-4669 miR-455
miR-425-3p miR-4721 miR-495
miR-449b-5p miR-4728-5p miR-518a
miR-450b-5p miR-4788 miR-520b
miR-484 miR-5739
miR-629-5p miR-6086
miR-651-5p miR-6125
miR-708-5p miR-642a-3p
Down-regulated miR-7 miR-34a-5p miR-1 miR-204-3p miR-151a-3p miR-15a-5p miR-486-3p miR-23a-3p miR-23a miR-92 miR-199a-5p miR-221
miR-15a miR-92b-3p miR-297 miR-491-5p miR-181b-5p miR-15b-5p miR-27a-3p miR-23b miR-320 miR-455-3p miR-654-5p
miR-15b miR-211-5p miR-4742-3p miR-619-5p miR-320a miR-16-5p miR-30e-5p miR-25 miR-363 miR-577 miR-656
miR-21 miR-3960 miR-3687 miR-328 miR-19b-3p miR-32-5p miR-29b miR-650
miR-23a miR-5096 miR-433 miR-20a-5p miR-30e
miR-27a miR-489 miR-92a-3p miR-93
miR-93 miR-572 miR-103a-3p miR-103
miR-95 miR-663a miR-195-5p miR-107
miR-128 miR-451a miR-185
miR-129 miR-574-3p miR-186
miR-132 miR-940 miR-191
miR-148b miR-1228-3p miR-194
TABLE 2 | Continued
Hicks and Middleton miRNA Biomarkers in Autism
Frontiers in Psychiatry | November 2016 | Volume 7 | Article 176
miR-212 miR-3613-3p miR-195
miR-431 miR-3935 miR-205
miR-484 miR-4436b-5p miR-342
miR-598 miR-4665-5p miR-346
miR-4700-3p miR-376a-AS
let-7a-5p miR-451
let-7d-5p miR-519c
let-7f-5p miR-524
Direction of change in different biomaterials
B. miRNA ID Seed
Human miRNAs with same seed CNS Blood Saliva Lymphoblast Olfactory
miR-7-5p GGAAGAC ↑↓ ↑
miR-10a-5p ACCCUGU miR-10a-5p miR-10b-5p ↑ ↑
miR-15a-5p AGCAGCA miR-15a-5p miR-15b-5p miR-16-5p miR-195-5p miR-424-5p miR-497-5p
↓ ↓
miR-15b-5p AGCAGCA miR-15a-5p miR-15b-5p miR-16-5p miR-195-5p miR-424-5p miR-497-5p
↓ ↓
miR-19b-3p GUGCAAA miR-19a-3p ↑ ↑↓
miR-21-3p AACACCA miR-3591-3p ↑↑
miR-23a-3p UCACAUU miR-23-3p miR-23b-3p miR-23c [miR-130a-5p] ↑↓ ↓ ↓↑
miR-27a-3p UCACAGU miR-27-3p miR-27b-3p ↑ ↓
miR-30e-5p GUAAACA miR-30a-5p miR-30b-5p miR-30c-5p miR-30d-5p miR-30-5p ↓ ↓↑
miR-92a-3p AUUGCAC miR-25-3p miR-32-5p miR-92-3p miR-92b-3p miR-363-3p miR-367-3p ↓ ↓
miR-92b-3p AUUGCAC miR-25-3p miR-32-5p miR-92-3p miR-92b-3p miR-363-3p miR-367-3p ↓ ↓
miR-93 AAAGUGC miR-17-5p miR-20a-5p miR-20b-5p miR-93-5p miR-106a-5p miR-106b-5p
miR-519d-3p miR-526b-3p
↓ ↓
miR-103a-3p GCAGCAU miR-103-3p ↓ ↓
miR-106b-5p AAAGUGC miR-17-5p miR-20a-5p miR-20b-5p miR-93-5p miR-106a-5p miR-519d-3p
↑ ↑
miR-132 AACAGUC miR-132-3p miR-212-3p ↑↑
CCGUGGC miR-132-5p
miR-140-3p ACCACAG ↑ ↑
miR-146a-5p GAGAACU miR-146a-5p miR-146b-5p [miR-7153-5p] ↑ ↑
miR-146b GAGAACU miR-146a-5p miR-146b-5p [miR-7153-5p] ↑ ↑
miR-155-5p UAAUGCU ↑↑
miR-195-5p AGCAGCA miR-15a-5p miR-15b-5p miR-16-5p miR-424-5p miR-497-5p [miR-6838-5p] ↓↑ ↓
miR-199b-5p CCAGUGU miR-199a-5p ↑↑
miR-219-5p GAUUGUC miR-219a-5p [miR-4782-3p] [miR-6766-3p] ↑ ↑
miR-320a AAAGCUG miR-320b miR-320c miR-320d [miR-4429] ↑ ↓
miR-335-3p UUUUCAU ↑ ↑
miR-338-5p ACAAUAU ↑ ↑
miR-451a AACCGUU ↑ ↓
miR-494 GAAACAU miR-494-3p ↑ ↑
GGUUGUC miR-494-5p miR-323b-5p miR-410-5p
% overlap 23.1 19.0 42.8 25.8 25.0
Boldface miRNAs change in the same direction in multiple tissues. Italic miRNAs change in different directions in multiple studies. Lower left indicates miRNAs that share the same seed sequence as those that appear changed in
multiple studies. Lower right indicates predominant direction of change in ASD subjects relative to controls for each biomaterial. For tissues with opposing changes both arrows are shown.
CNS, central nervous system; SCs, stem cells.
The nal row shows the proportion of altered miRNAs for each biomaterial that overlap with other studies.
Hicks and Middleton miRNA Biomarkers in Autism
Frontiers in Psychiatry | November 2016 | Volume 7 | Article 176
Expression of miRNA across Brain
MicroRNA expression is a dynamic process, changing through-
out development and across brain regions (43). A study of
non-coding RNA in the superior temporal gyrus of ASD subjects
demonstrated changes in regional miRNA proles that could
contribute to ASD (60). Specically, while miRNA patterns in the
superior temporal sulcus (a region relevant to social interaction)
and primary auditory cortex changed throughout development
in the typical brain, many of the changes were blunted in subjects
with ASD. Four of the individual miRNAs identied (miR-93-3p,
miR-103, miR-132, and miR-320) were among the 219 miRNAs
altered in other studies of ASD, with 3 of the 4 changed in multiple
studies (Table2).
Potential Mechanisms for miRNA
e molecular mechanisms underlying miRNA dysregulation
in ASD are still being explored. One explanation is that autistic
miRNA patterns may represent a permanent response to an early
environmental insult, such as perinatal hypoxia (61). In this sce-
nario, miRNA regulation could be disrupted by a layer of epigenetic
machinery such as DNA methylation. In a study of 12 subjects
with ASD, expression of miR-142 was increased in Brodmann’s
area 10 compared with healthy controls – a change accompanied
by hypomethylation of the miR-142 promoter region (53).
Another possible mechanism is that individual miRNAs
sequences are altered in children with ASD. Pooled analysis of
common and rare single nucleotide polymorphisms (SNPs) in
449 cases of ASD using whole exome sequencing has identied 2
miRNA clusters: miR-133b/miR-206 and mir-17/miR/18a/miR-
19a/miR20a/miR-19b-1/miR-92a (62). Four of these individual
components have disrupted expression levels in children with
ASD, and miR-92a is downregulated in multiple studies (9, 14).
Yet another explanation could be that the location of specic
miRNAs at CNV loci leads to their dysregulation. Of the 41 CNV
loci associated with ASD, >10% contain miRNAs (63). ree of
the 10 “hub miRNAs” (i.e., miRNAs at CNV loci with the great-
est gene network connectivity) have been previously identied
in studies of ASD (miR-34a, miR-548, and miR-195) and target
multiple genes involved in neurodevelopment. Alterations in
miR-195 have been reported in serum (13, 14) and lymphoblasts
(10) of ASD individuals. Furthermore, a key gene involved in
miRNA synthesis (Dicer) was identied as a common target
for many of the hub miRNAs, suggesting that some CNVs in
ASD might cause extensive dysregulation of the entire miRNA
Systemic miRNA Dysregulation
Although the brain is considered the primary pathological target
in ASD, alterations in miRNA expression have been noted in a
number of other body sites (13, 15). is may explain (in part)
why children with ASD experience medical comorbidities out-
side the CNS, including gastrointestinal issues such as esophageal
reux and constipation (64). ey also have altered oral-motor
function (speech apraxia) and sensory processing (65, 66). Given
these comorbidities, it should come as no surprise that oral
miRNA patterns are altered in children with ASD. In addition,
the proximity of the oropharynx to the CNS and its abundant
sensory and motor nerve innervation (involving branches of the
glossopharyngeal, facial, vagus, and trigeminal nerves) could
provide an ample source of miRNA (67, 68).
Alterations in miRNA proles of peripheral blood sites may be
explained by circulating miRNA that has crossed the BBB (espe-
cially considering that ASD is not associated with hematologic
disorders). In peripheral sources such as blood, brain-related
miRNA may be stable and abundant as a result of exosomal trans-
port (13, 14). us, while biouids such as serum and saliva may
not represent optimal models for studying CNS pathophysiology,
they may represent ideal reservoirs for ASD biomarkers.
Employing miRNAs as Biomarkers
e potential of miRNAs as biomarkers in ASD has been explored
in studies of serum and saliva. In a study of serum miRNAs from
55 children with ASD, the authors identied three miRNAs (miR-
130-3p, miR-181b-5p, and miR-320a) with an area under the
curve (AUC) >0.85 (13). In our own more recent study, involving
24 children with ASD, a set of 14 salivary miRNAs showed more
than 95% accuracy (AUC=0.92) at dierentiating control and
ASD subjects (15). is study also showed that expression pat-
terns of individual salivary miRNAs were signicantly correlated
with several measures of adaptive behavior. is highlights how
the utility of miRNA extends beyond simple ASD diagnosis and
may 1day be used to predict ASD phenotype and severity.
It is clear that miRNA proles are dysregulated across multiple
tissue types in subjects with ASD. is review encapsulated 219
target miRNAs from 12 human studies of ASD and identied 27
that were dysregulated in 2 investigations. Functional pathway
analysis supports the idea that these miRNAs target brain-
expressed genes related to neurodevelopment and implicated
in ASD. ree miRNAs showed consistent dysregulation across
3 studies (miR-23a-3p, miR-146a-5p, and miR-106b-5p). e
networks of genes targeted by these miRNAs are implicated in
ASD and have signicant roles in neurotrophin signaling. BDNF,
which is important for hippocampal neurogenesis and decreased
in the serum of adult ASD patients (59), is targeted by 6 of the
27 miRNAs of interest. Animal and cell models that assess these
molecular mechanisms behind miRNAs and their mRNA targets
will be critical in advancing the current ASD knowledge-base.
is manuscript involves a review and analysis of current lit-
erature collected in preparation for the study “Improving Autism
Screening with Brain-Related miRNA,” which has been approved
by the Institutional Review Board at the Penn State College of
Medicine, Hershey, PA, USA (STUDY00003658). According
to the publications on which this review was based, informed
consent or assent was obtained from all participants or their par-
ents, unless the study involved exclusively postmortem human
Hicks and Middleton miRNA Biomarkers in Autism
Frontiers in Psychiatry | November 2016 | Volume 7 | Article 176
brain material. In the case of minor children unable to provide
informed assent, parental consent was obtained.
SH and FM conceived of this study, prepared the gures and
tables, and wrote the manuscript.
We thank R. Uhlig, C. Dowd-Greene, R. Carpenter, C. Ignacio,
T. Welch, and K. Gentile for their creative and technical insights
and support of this work.
is work was supported by the Department of Pediatrics at
Penn State College of Medicine, the Department of Pediatrics
at SUNY Upstate Medical University, and research grants
from Motion Intelligence Inc., the National Institute of
Mental Health (MH111347), and the Kirson-Kolodner-Fedder
Charitable Fund.
e Supplementary Material for this article can be found online
FIGURE S1 | Protein–protein interaction (PPI) network of the genes that
are targeted by 2 or more of at least 20 of the 27 conserved miRNAs
affected across studies. This analysis involved mapping of 199 mRNA targets
in the STRING v10 database ( Note the presence of a
signicant interaction network containing 137 edges for the 197 targets with
annotation information available. Notably, only genes with connections are
included in the PPI. Also, note that many of these genes were involved in
brain-related functions, including some that mapped to a neuronal projection
Gene Ontology (highlighted in red).
1. Lee RC, Feinbaum RL, Ambros V. e C. elegans heterochronic gene lin-4
encodes small RNAs with antisense complementarity to lin-14. Cell (1993)
75(5):843–54. doi:10.1016/0092-8674(93)90529-Y
2. Wightman B, Ha I, Ruvkun G. Posttranscriptional regulation of the heteroch-
ronic gene lin-14 by lin-4 mediates temporal pattern formation in C.elegans.
Cell (1993) 75(5):855–62. doi:10.1016/0092-8674(93)90530-4
3. Lau NC, Lim LP, Weinstein EG, Bartel DP. An abundant class of tiny RNAs
with probable regulatory roles in Caenorhabditis elegans. Science (2001)
294(5543):858–62. doi:10.1126/science.1065062
4. Rodriguez A, Griths-Jones S, Ashurst JL, Bradley A. Identication of mam-
malian microRNA host genes and transcription units. Genome Res (2004)
14(10a):1902–10. doi:10.1101/gr.2722704
5. Schratt G. microRNAs at the synapse. Nat Rev Neurosci (2009) 10(12):842–9.
6. Kos A, Loohuis NO, Meinhardt J, van Bokhoven H, Kaplan BB, Martens
GJ, et al. MicroRNA-181 promotes synaptogenesis and attenuates axonal
outgrowth in cortical neurons. Cell Mol Life Sci (2016) 73(18):3555–67.
7. Wang W, Kwon EJ, Tsai LH. MicroRNAs in learning, memory, and
neurological diseases. Learn Mem (2012) 19(9):359–68. doi:10.1101/lm.
8. Abu-Elneel K, Liu T, Gazzaniga FS, Nishimura Y, Wall DP, Geschwind DH,
etal. Heterogeneous dysregulation of microRNAs across the autism spectrum.
Neurogenetics (2008) 9(3):153–61. doi:10.1007/s10048-008-0133-5
9. Talebizadeh Z, Butler MG, eodoro MF. Feasibility and relevance of exam-
ining lymphoblastoid cell lines to study role of microRNAs in autism. Autism
Res (2008) 1(4):240–50. doi:10.1002/aur.33
10. Sarachana T, Zhou R, Chen G, Manji HK, Hu VW. Investigation of post-tran-
scriptional gene regulatory networks associated with autism spectrum disor-
ders by microRNA expression proling of lymphoblastoid cell lines. Genome
Med (2010) 2(4):23. doi:10.1186/gm144
11. Seno MMG, Hu P, Gwadry FG, Pinto D, Marshall CR, Casallo G, etal. Gene
and miRNA expression proles in autism spectrum disorders. Brain Res
(2011) 1380:85–97. doi:10.1016/j.brainres.2010.09.046
12. Popov NT, Madjirova NP, Minkov IN, Vachev TI. MicroRNA HSA-
486-3P gene expression proling in the whole blood of patients with
autism. Biotechnol Biotechnol Equip (2012) 26(6):3385–8. doi:10.5504/
13. Vasu MM, Anitha A, anseem I, Suzuki K, Yamada K, Takahashi T, etal.
Serum microRNA proles in children with autism. Mol Autism (2014) 5:40.
14. Huang F, Long Z, Chen Z, Li J, Hu Z, Qiu R, etal. Investigation of gene regu-
latory networks associated with autism spectrum disorder based on MiRNA
expression in China. PLoS One (2015) 10(6):e0129052. doi:10.1371/journal.
15. Hicks SD, Ignacio C, Gentile K, Middleton FA. Salivary miRNA proles
identify children with autism spectrum disorder, correlate with adaptive
behavior, and implicate ASD candidate genes involved in neurodevelopment.
BMC Pediatr (2016) 16:52. doi:10.1186/s12887-016-0586-x
16. Nguyen LS, Lepleux M, Makhlouf M, Martin C, Fregeac J, Siquier-Pernet K,
et al. Proling olfactory stem cells from living patients identies miRNAs
relevant for autism pathophysiology. Mol Autism (2016) 7(1):1. doi:10.1186/
17. Jin XF, Wu N, Wang L, Li J. Circulating microRNAs: a novel class of
potential biomarkers for diagnosing and prognosing central nervous
system diseases. Cell Mol Neurobiol (2013) 33(5):601–13. doi:10.1007/
18. Volkmar FR, Reichow B, Westphal A, Mandell DS. Autism and the autism
spectrum: diagnostic concepts. In: Volkmar FR, editor. Handbook of Autism
and Pervasive Developmental Disorders. 4th ed. Hoboken, NJ: John Wiley &
Sons Inc. (2014). doi:10.1002/9781118911389.hautc01
19. Tsatsanis KD, Volkmar FR, Paul R, Klin A, Cohen D. Neuropsychological
characteristics in autism and related conditions. In: Volkmar FR, Paul R,
Klin A, Cohen D, editors. Handbook of Autism and Pervasive Developmental
Disorders: Diagnosis, Development, Neurobiology, and Behavior. Vol. 1, 3rd ed.
Hoboken, NJ: John Wiley & Sons Inc. (2014). p. 365–81. doi:10.1002/
20. Developmental Disabilities Monitoring Network Surveillance Year 2010
Principal Investigators, Centers for Disease Control and Prevention (CDC).
Prevalence of autism spectrum disorder among children aged 8 years-autism
and developmental disabilities monitoring network, 11 sites, United States,
2010. MMWR Surveill Summ (2014) 63(2):1–21.
21. Barad DH, Kushnir VA, Albertini D, Gleicher N. CDC analysis of ICSI/autism:
association is not causation. Hum Reprod (2015) 30(7):1745–6. doi:10.1093/
22. Huguet G, Benabou M, Bourgeron T. e Genetics of Autism Spectrum
Disorders. In a Time for Metabolism and Hormones. New York, NY: Springer
International Publishing (2016). p. 101–29.
23. Bailey A, Le Couteur A, Gottesman I, Bolton P, Simono E, Yuzda E, etal.
Autism as a strongly genetic disorder: evidence from a British twin study.
Psychol Med (1995) 25(01):63–77. doi:10.1017/S0033291700028099
24. Pickles A, Bolton P, Macdonald H, Bailey A, Le Couteur A, Sim CH, et al.
Latent-class analysis of recurrence risks for complex phenotypes with selec-
tion and measurement error: a twin and family history study of autism. Am
J Hum Genet (1995) 57(3):717.
25. Gaugler T, Klei L, Sanders SJ, Bodea CA, Goldberg AP, Lee AB, et al. Most
genetic risk for autism resides with common variation. Nat Genet (2014)
46(8):881–5. doi:10.1038/ng.3039
Hicks and Middleton miRNA Biomarkers in Autism
Frontiers in Psychiatry | November 2016 | Volume 7 | Article 176
26. Huguet G, Ey E, Bourgeron T. e genetic landscapes of autism spectrum
disorders. Annu Rev Genomics Hum Genet (2013) 14:191–213. doi:10.1146/
27. De Rubeis S, Buxbaum JD. Recent advances in the genetics of autism
spectrum disorder. Curr Neurol Neurosci Rep (2015) 15(6):1–9. doi:10.1007/
28. Basu SN, Kollu R, Banerjee-Basu S. AutDB: a gene reference resource for
autism research. Nucleic Acids Res (2009) 37:D832–6. doi:10.1093/nar/
29. Mazzio EA, Soliman KF. Basic concepts of epigenetics: impact of envi-
ronmental signals on gene expression. Epigenetics (2012) 7(2):119–30.
30. Miyake K, Hirasawa T, Koide T, Kubota T. Epigenetics in autism and other
neurodevelopmental diseases. In: Ahmad SI, editor. Neurodegenerative
Diseases. USA: Springer (2012). p. 91–8.
31. Rajalakshmi K. Epigenetics as a solution in autism: control above autism
genes. Autism Open Access (2015) 5:e130. doi:10.4172/2165-7890.1000e130
32. Ross SA, Davis CD. e emerging role of microRNAs and nutrition in mod-
ulating health and disease. Annu Rev Nutr (2014) 34:305–36. doi:10.1146/
33. Chen F, Hu SJ. Eect of microRNA-34a in cell cycle, dierentiation, and
apoptosis: a review. J Biochem Mol Toxicol (2012) 26(2):79–86. doi:10.1002/
34. Friedländer MR, Lizano E, Houben AJ, Bezdan D, Báñez-Coronel M, Kudla G,
etal. Evidence for the biogenesis of more than 1,000 novel human microRNAs.
Genome Biol (2014) 15(4):R57. doi:10.1186/gb-2014-15-4-r57
35. Agarwal V, Bell GW, Nam JW, Bartel DP. Predicting eective microRNA
target sites in mammalian mRNAs. Elife (2015) 4:e05005. doi:10.7554/eLife.
36. Sun E, Shi Y. MicroRNAs: small molecules with big roles in neurode-
velopment and diseases. Exp Neurol (2015) 268:46–53. doi:10.1016/j.
37. Tritschler F, Huntzinger E, Izaurralde E. Role of GW182 proteins and PABPC1
in the miRNA pathway: a sense of déjà vu. Nat Rev Mol Cell Biol (2010)
11:379–84. doi:10.1038/nrm2885
38. Brennecke J, Stark A, Russell RB, Cohen SM. Principles of microRNA–
target recognition. PLoS Biol (2005) 3(3):e85. doi:10.1371/journal.pbio.
39. Friedman RC, Farth KK, Burge CB, Bartel DP. Most mammalian mRNAs are
conserved targets of miRNAs. Genome Res (2009) 19(1):92–105. doi:10.1101/
40. Weber JA, Baxter DH, Zhang S, Huang DY, Huang KH, Lee MJ, etal. e
miRNA spectrum in 12 body uids. Clin Chem (2010) 56(11):1733–41.
41. Adlakha YK, Saini N. Brain miRNAs and insights into biological functions
and therapeutic potential of brain enriched miRNA-128. Mol Cancer (2014)
13:33. doi:10.1186/1476-4598-13-33
42. Ziats MN, Rennert OM. Identication of dierentially expressed microRNAs
across the developing human brain. Mol Psychiatry (2014) 19(7):848–52.
43. Tonelli DDP, Pulvers JN, Haner C, Murchison EP, Hannon GJ, Huttner WB.
miRNAs are essential for survival and dierentiation of newborn neurons
but not for expansion of neural progenitors during early neurogenesis in
the mouse embryonic neocortex. Development (2008) 135(23):3911–21.
44. Davis TH, Cuellar TL, Koch SM, Barker AJ, Harfe BD, McManus MT, et al.
Conditional loss of Dicer disrupts cellular and tissue morphogenesis in the
cortex and hippocampus. J Neurosci (2008) 28(17):4322–30. doi:10.1523/
45. Im HI, Kenny PJ. MicroRNAs in neuronal function and dysfunction. Trends
Neurosci (2012) 35(5):325–34. doi:10.1016/j.tins.2012.01.004
46. Karam R, Wilkinson M. A conserved microRNA/NMD regulatory circuit
controls gene expression. RNA Biol (2012) 9(1):22–6. doi:10.4161/rna.9.1.
47. Zongaro S, Hukema R, D’Antoni S, Davidovic L, Barbry P, Catania MV, etal.
e 3 UTR of FMR1 mRNA is a target of miR-101, miR-129-5p and miR-221:
implications for the molecular pathology of FXTAS at the synapse. Hum Mol
Genet (2013) 22(10):1971–82. doi:10.1093/hmg/ddt044
48. Schoeld CM, Hsu R, Barker AJ, Gertz CC, Blelloch R, Ullian EM. Monoallelic
deletion of the miRNA biogenesis gene Dgcr8 produces decits in the develop-
ment of excitatory synaptic transmission in the prefrontal cortex. Neural Dev
(2011) 6(1):11. doi:10.1186/1749-8104-6-11
49. Fénelon K, Mukai J, Xu B, Hsu PK, Drew LJ, Karayiorgou M, etal. Deciency
of Dgcr8, a gene disrupted by the 22q11.2 microdeletion results in altered
short-term plasticity in the prefrontal cortex. Proc Natl Acad Sci U S A (2011)
108(1):4447–52. doi:10.1073/pnas.1101219108
50. Issler O, Chen A. Determining the role of microRNAs in psychiatric disorders.
Nat Rev Neurosci (2015) 16(4):201–12. doi:10.1038/nrn3879
51. Boccuto L, Lauri M, Sarasua SM, Skinner CD, Buccella D, Dwivedi A, etal.
Prevalence of SHANK3 variants in patients with dierent subtypes of autism
spectrum disorders. Eur J Hum Genet (2013) 21(3):310–6. doi:10.1038/
52. Wu YE, Parikshak NN, Belgard TG, Geschwind DH. Genome-wide, integra-
tive analysis implicates microRNA dysregulation in autism spectrum disorder.
Nat Neurosci (2016). doi:10.1038/nn.4373
53. Mor M, Nardone S, Sams DS, Elliott E. Hypomethylation of miR-142
promoter and upregulation of microRNAs that target the oxytocin receptor
gene in the autism prefrontal cortex. Mol Autism (2015) 6(1):1. doi:10.1186/
54. Ander BP, Barger N, Stamova B, Sharp FR, Schumann CM. Atypical miRNA
expression in temporal cortex associated with dysregulation of immune, cell
cycle, and other pathways in autism spectrum disorders. Mol Autism (2015)
6:37. doi:10.1186/s13229-015-0029-9
55. Wong N, Wang X. miRDB: an online resource for microRNA target predic-
tion and functional annotations. Nucleic Acids Res (2015) 43(D1):D146–52.
56. Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis
of large gene lists using DAVID bioinformatics resources. Nat Protoc (2009)
4(1):44–57. doi:10.1038/nprot.2008.211
57. Hutcheson HB, Olson LM, Bradford Y, Folstein SE, Santangelo SL, SutclieJS,
etal. Examination of NRCAM, LRRN3, KIAA0716, and LAMB1 as autism
candidate genes. BMC Med Genet (2004) 5(1):1. doi:10.1186/1471-2156-5-1
58. Nakamura F, Kalb RG, Strittmatter SM. Molecular basis of semaphorin-
mediated axon guidance. J Neurobiol (2000) 44(2):219–29. doi:10.1002/1097-
59. Hashimoto K, Iwata Y, Nakamura K, Tsujii M, Tsuchiya KJ, Sekine Y, et al.
Reduced serum levels of brain-derived neurotrophic factor in adult male
patients with autism. Prog Neuropsychopharmacol Biol Psychiatry (2006)
30(8):1529–31. doi:10.1016/j.pnpbp.2006.06.018
60. Stamova B, Ander BP, Barger N, Sharp FR, Schumann CM. Specic regional
and age-related small Noncoding RNA expression patterns within superior
temporal gyrus of typical human brains are less distinct in autism brains.
J Child Neurol (2015) 30(14):1930–46. doi:10.1177/0883073815602067
61. Froehlich-Santino W, Tobon AL, Cleveland S, Torres A, Phillips J, Cohen B,
etal. Prenatal and perinatal risk factors in a twin study of autism spectrum
disorders. J Psychiatr Res (2014) 54:100–8. doi:10.1016/j.jpsychires.2014.
62. Toma C, Torrico B, Hervás A, Salgado M, Rueda I, Valdés-Mas R, et al.
Common and rare variants of microRNA genes in autism spectrum disorders.
World J Biol Psychiatry (2015) 16(6):376–86. doi:10.3109/15622975.2015.
63. Vaishnavi V, Manikandan M, Tiwary BK, Munirajan AK. Insights on
the functional impact of microRNAs present in autism-associated copy
number variants. PLoS One (2013) 8(2):e56781. doi:10.1371/journal.pone.
64. Chaidez V, Hansen RL, Hertz-Picciotto I. Gastrointestinal problems in
children with autism, developmental delays or typical development. J Autism
Devel Disor (2014) 44(5):1117–27. doi:10.1007/s10803-013-1973-x
65. Cermak SA, Curtin C, Bandini L. Sensory sensitivity and food selectivity in
children with autism spectrum disorders. In: Patel VB, Preedy VR, Martin
CR, editors. Comprehensive Guide to Autism. New York: Springer (2014).
p. 2061–76.
66. Tierney C, Mayes S, Lohs SR, Black A, Gisin E, Veglia M. How valid
is the checklist for autism spectrum disorder when a child has apraxia
of speech? J Dev Behav Pediatr (2015) 36(8):569–74. doi:10.1097/DBP.
Hicks and Middleton miRNA Biomarkers in Autism
Frontiers in Psychiatry | November 2016 | Volume 7 | Article 176
67. Miller M, Chukoskie L, Zinni M, Townsend J, Trauner D. Dyspraxia, motor
function and visual–motor integration in autism. Behav Brain Res (2014)
269:95–102. doi:10.1016/j.bbr.2014.04.011
68. Majem B, Rigau M, Reventós J, Wong DT. Non-coding RNAs in saliva: emerg-
ing biomarkers for molecular diagnostics. Int J Mol Sci (2015) 16(4):8676–98.
Conict of Interest Statement: SH and FM hold a provisional patent for the use
of a panel of specic miRNAs in the saliva as biomarkers for autism. e patent
was led by the State University of New York, Upstate Medical University, and has
been licensed to Motion Intelligence, Inc. SH also receives consulting fees from
Motion Intelligence, Inc. ese interests have been disclosed and are under active
management by the Penn State Conict of Interest Committee.
Copyright © 2016 Hicks and Middleton. is is an open-access article distributed
under the terms of the Creative Commons Attribution License (CC BY). e use,
distribution or reproduction in other forums is permitted, provided the original
author(s) or licensor are credited and that the original publication in this journal
is cited, in accordance with accepted academic practice. No use, distribution or
reproduction is permitted which does not comply with these terms.
... ; doi: bioRxiv preprint implicated in neuropathological conditions such as ASD 15,16 . In the assembled Kayseri cohort, miRNA PCR Array profiles of the 100 expression of 372 miRNAs (see Ozkul et al. 13 for expression and list of the microRNAs) led to the striking observation of a reduced expression of six of them among 372 miRNAs compared to healthy controls. ...
Full-text available
The growing burden of a gradual increase in births of children with autism has placed it at the center of the concerns of major laboratories. We have previously detected a decrease in the levels of six miRNAs (miR-19a-3p, miR-361-5p, miR-3613-3p, miR-150-5p, miR-126-3p, and miR-499a-5p) in parents and their children inherited at a lower level. Here, we suggest that down-regulation of each of these six miRNAs inherited from parents contributes to the development of children with autism. We compare their levels of distribution in each family between the autistic child and siblings. We find that the distribution of levels of these miRNAs in siblings (undiagnosed as autism) is not always higher than in autistic children, but it is at varying levels. These data support a model in which autistic behavior relies on low levels of the six miRNAs expressed in children potentially associated with autistic syndrome (ASD). The intimate link between miRNAs levels and behavioral characteristics suggests possibilities for understanding the basic circuitry involved in autism and thus advancing partial knowledge of brain functions. An early diagnosis of autism helps provide children an environment conducive to their development. Graphical abstract
... Elevated levels of miR-34c-5p, miR-92a-2-5p, miR-145-5p and miR-199a-5p, as well as reduced levels of miR-27a-3p, miR-19b-1-5p and miR-193a-5p are observed in the serum of ASD patients [14]. In the postmortem cerebellar cortex, increased miR-23a and decreased miR-106b have been reported [15]. These findings suggest miRNAs are promising potential biomarkers for the diagnosis of ASD. ...
Full-text available
Patients with autism spectrum disorder (ASD) typically experience substantial social isolation, which may cause secondary adverse effects on their brain development. miR-124 is the most abundant miRNA in the human brain, acting as a pivotal molecule regulating neuronal fate determination. Alterations of miR-124 maturation or expression are observed in various neurodevelopmental, neuropsychiatric, and neurodegenerative disorders. In the present study, we analyzed a panel of brain-enriched microRNAs in serums from 2 to 6 year old boys diagnosed with ASD. The hsa-miR-124 level was found significantly elevated in ASD boys than in age and sex-matched healthy controls. In an isolation-reared weanling mouse model, we evidenced elevated mmu-miR-124 level in the serum and the medial prefrontal cortex (mPFC). These mice displayed significant sociability deficits, as well as myelin abnormality in the mPFC, which was partially rescued by expressing the miR-124 sponge in the bilateral mPFC, ubiquitously or specifically in oligodendroglia. In cultured mouse oligodendrocyte precursor cells, introducing a synthetic mmu-miR-124 inhibited the differentiation process through suppressing expression of nuclear receptor subfamily 4 group A member 1 (Nr4a1). Overexpressing Nr4a1 in the bilateral mPFC also corrected the social behavioral deficits and myelin impairments in the isolation-reared mice. This study revealed an unanticipated role of the miR-124/Nr4a1 signaling in regulating early social experience-dependent mPFC myelination, which may serve as a potential therapy target for social neglect or social isolation-related neuropsychiatric disorders.
... While some evidence suggests that heredity is more important in the etiology of autism spectrum disorder (ASD) than environmental variables, new advances in genetic discoveries have helped to enhance our knowledge of the etiology of ASD (Kim & Leventhal, 2015). Various studies have recognized the genetic origin of ASD and associated neurobiological processes at numerous levels, from molecules to circuits (Hicks & Middleton, 2016). On the basis of solid genetic evidence, only approximately 65 genes out of hundreds are known to be implicated with ASD (Sanders et al., 2015). ...
Full-text available
Autism spectrum disorder (ASD) is a complex neurodevelopmental condition. Genetic, environmental, and epigenetic variables are all likely to have a role in the occurrence of ASD. This systematic review was done to determine the implications of genetic factors and modifiers in ASD. Our results show that nearly all human chromosomes have one or more genes susceptible to autism including X and Y chromosomes. In majority of the studies, different genes like MTHFR, A1298C, KDM5B, AIM2, AMPD1-NRAS, TRIM33, and TRIM33-BCAS2 located on chromosome number 1 were found to have high association with ASD. It is concluded that genes on approximately all human chromosomes have association with the risk of ASD. Given the possible involvement of epigenetic processes in the development of autism and the ability of environmental variables to change gene expression, it seems essential to investigate a variety of factors, particularly interaction between gene and environment.
... Since its function of orchestrating genetic spatiotemporal expression in the transitional processes during neurodevelopment, it provides a different perspective to elucidate the complex role of neurotransmitter chaos in mental disorders. Also, it was found to be related to several neurodevelopmental diseases such as Autism Spectrum Disorder (ASD) and schizophrenia for its continuous effect on the process of neurogenesis and synaptic plasticity (26,27). A few studies revealed the value of circulating miRNAs supposed to be biomarkers of OCD (28,29). ...
Full-text available
Obsessive-compulsive disorder (OCD) is a deliberating disorder with complex genetic and environmental etiologies. Hypotheses about OCD mainly include dysregulated neurotransmitters, especially serotonin, and disturbed neurodevelopment. Single nucleotide polymorphism (SNP) association studies regarding OCD are often met with inconsistent results. However, stratification by age of onset may sometimes help to limit the heterogenicity of OCD patients. Therefore, we conducted a stratified SNP association study enrolling 636 patients and 612 healthy controls. Patients were stratified by age of onset as early-onset (EO-OCD) and late-onset (LO-OCD). Blood extracted from the patients was used to genotype 18 loci, including serotonin system genes, Slitrk1, Slitrk5, and DMRT2 and related miRNA genes. Logistic regression was used to compare allele and genotype frequencies of variants. A general linear model was used to evaluate the association between variants and trait anxiety. In our study, rs3824419 in DMRT2 was associated with EO-OCD, G allele was the risk allele. Rs2222722 in miR-30a-5p was associated with EO-OCD, with the C allele being the risk allele. Rs1000952 in HTR3D was found associated with trait anxiety in OCD patients. The significance disappeared after FDR correction. Our results supported neurodevelopment-related genes, DMRT2 and miR-30a-5p, to be related to EO-OCD. However, we cannot prove serotonin genes to be directly associated with EO-OCD. While an association between HTR3D and trait anxiety was discovered, comparisons based on biological or clinical traits may be helpful in future studies. As our detective powers were limited, more large-scale studies will be needed to confirm our conclusion.
... Several studies have shown that hsa-miR-30e-5p among other miRNAs might be associated with the onset and progression of Parkinson's disease and schizophrenia [54][55][56] Our results also showed deregulated hsa-miR-191-5p. Although, as far as we are aware, no evidence for hsa-miR-191-5p contribution to FXS has been reported so far, alterations in the expression level of hsa-miR-191-5p have earlier been found in patients with neuropsychiatric disorders sharing genetic overlap with FXS, including ASD, ADHD, schizophrenia, bipolar disorder, and major depressive disorder 49,50,61,62 . ...
Full-text available
Fragile X syndrome (FXS) is caused by a mutation in the FMR1 gene which can lead to a loss or shortage of the FMR1 protein. This protein interacts with specific miRNAs and can cause a range of neurological disorders. Therefore, miRNAs could act as a novel class of biomarkers for common CNS diseases. This study aimed to test this theory by exploring the expression profiles of various miRNAs in Iranian using deep sequencing-based technologies and validating the miRNAs affecting the expression of the FMR1 gene. Blood samples were taken from 15 patients with FXS (9 males, 6 females) and 12 controls. 25 miRNAs were differentially expressed in individuals with FXS compared to controls. Levels of 9 miRNAs were found to be significantly changed (3 upregulated and 6 downregulated). In Patients, the levels of hsa-miR-532-5p, hsa-miR-652-3p and hsa-miR-4797-3p were significantly upregulated while levels of hsa-miR-191-5p, hsa-miR-181-5p, hsa-miR-26a-5p, hsa-miR-30e-5p, hsa-miR-186-5p, and hsa-miR-4797-5p exhibited significant downregulation; and these dysregulations were confirmed by RT‐qPCR. This study presents among the first evidence of altered miRNA expression in blood samples from patients with FXS, which could be used for diagnostic, prognostic, and treatment purposes. Larger studies are required to confirm these preliminary results.
... The miRNA expression patterns are varied depending from organs ranging from brain, saliva, blood and olfactory Department of Knowledge and Research Support Services Volume 1 Issue 2, 2021 precursor cells of patients effected with ASD. At present, several identified miRNAs show potential involvement in ASD [11]. According to previous studies, miRNAs are strongly associated with the pathogenesis of ASD [12]. ...
Full-text available
Autism spectrum disorder (ASD) is a complex group of neurodevelopmental disorders encompassing perturbations in verbal and non-verbal communication, social skills, as well as repetitive and restricted behaviour, activity, or response. The pathogenesis of the disorder is still unknown, yet several studies have documented the involvement of both genetic and environmental factors in its onset. Intense efforts have been made to identify reliable biomarkers to aid in early diagnosis. MicroRNAs (miRNAs) are the regulatory noncoding regions of ribonucleic acid (RNA) that can alter the expression of a gene through posttranscriptional mechanisms. In this study, an in-silico technique was used to identify two novel biomarkers, namely miR-106b-5p and miR-93-5p. The analysis identified that these diagnostic biomarkers are associated with ASD and can aid in its early treatment since miRNAs play a significant role in the development and function of the central nervous system (CNS).
... miRNAs are not translated into proteins however; they have crucial roles in regulatory steps of cellular mechanisms. It is estimated that approximately 2/3 of human mRNA is regulated by miRNAs and the ratio of the genes regulated by miRNAs are above 60% meaning that miRNAs are involved in the translation of 60 proteins in every 100 proteins [4,5]. ...
In the past decade, growing interest in micro-ribonucleic acids (miRNAs) has catapulted these small, non-coding nucleic acids to the forefront of biomarker research. Advances in scientific knowledge have made it clear that miRNAs play a vital role in regulating cellular physiology throughout the human body. Perturbations in miRNA signaling have also been described in a variety of pediatric conditions—from cancer, to renal failure, to traumatic brain injury. Likewise, the number of studies across pediatric disciplines that pair patient miRNA-omics with longitudinal clinical data are growing. Analyses of these voluminous, multivariate data sets require understanding of pediatric phenotypic data, data science, and genomics. Use of machine learning techniques to aid in biomarker detection have helped decipher background noise from biologically meaningful changes in the data. Further, emerging research suggests that miRNAs may have potential as therapeutic targets for pediatric precision care. Here, we review current miRNA biomarkers of pediatric diseases and studies that have combined machine learning techniques, miRNA-omics, and patient health data to identify novel biomarkers and potential therapeutics for pediatric diseases. In the following review article, we summarized how recent developments in microRNA research may be coupled with machine learning techniques to advance pediatric precision care. In the following review article, we summarized how recent developments in microRNA research may be coupled with machine learning techniques to advance pediatric precision care.
Background and aims: Prenatal fine particulate matter (PM2.5) exposure has been linked to adverse neurodevelopment. However, epidemiological evidence remains inconclusive and little information about the effects of various PM2.5 components on child neurodevelopment is currently known. The underlying mechanism was also not elucidated. The study aimed to evaluate the effects of PM2.5 and components exposure on child neurodevelopmental delays and the role of placental small extracellular vesicles (sEVs)-derived miRNAs in the associations. Methods: We included 267 mother-child pairs in this analysis. Prenatal PM2.5 and components (i.e. elements, water-soluble ions, and PAHs) exposure during three trimesters were monitored through personal PM2.5 sampling. Child neurodevelopment at 2, 6, and 12 months old were evaluated by Ages and Stages Questionnaire (ASQ). We isolated sEVs from placental tissue to analyze the change of sEVs-derived miRNAs in response to PM2.5. Associations between the PM2.5-associated miRNAs and child neurodevelopment were evaluated using multivariate linear regression models. Results: The PM2.5 exposure levels in the three trimesters range from 2.51 to 185.21 μg/m3. Prenatal PM2.5 and the components of Pb, Al, V and Ti exposure in the second and third trimester were related to decreased ASQ scores communication, problem-solving and personal-social domains in children aged 2 or 6 months. RNA sequencing identified fifteen differentially expressed miRNAs. The miR-101-3p and miR-520d-5p were negatively associated with PM2.5 and Pb component. miR-320a-3p expression was positively associated with PM2.5 and V component. Meanwhile, the miR-320a-3p was associated with decreased ASQ scores, as reflected by ASQ-T (β: -2.154, 95 % CI: -4.313, -0.516) and problem-solving domain (β: -0.605, 95 % CI: -1.111, -0.099) in children aged 6 months. Conclusion: Prenatal exposure to PM2.5 and its Pb, Al, V & Ti component were associated with infant neurodevelopmental delays. The placenta sEVs derived miRNAs, especially miR-320a-3p, might contribute to an increased risk of neurodevelopmental delays.
Insufficient tools exist to aid the diagnosis and clinical management of traumatic brain injury. Emerging evidence suggests that measurement of micro ribonucleic acids may be used to guide identification of mild traumatic brain injury, and support prognoses for patients across a range of traumatic brain injury severity. Micro-ribonucleic acids are small, non-coding nucleic acids that control translation of specific proteins in response to environmental conditions, such as traumatic brain injury. They may be released from neurons and glia as a signaling tool following traumatic brain injury. Packaged within protective vesicles, or exosomes, extracellular micro-ribonucleic acids may experience perturbations after traumatic brain injury that convey information about the severity of injury, or the trajectory of brain recovery. Here, we discuss the current state of scientific knowledge regarding micro-ribonucleic acid biomarkers in traumatic brain injury, identify specific micro-ribonucleic acids with the greatest physiologic potential, and chart future directions to advance this budding field.
Full-text available
MicroRNA targets are often recognized through pairing between the miRNA seed region and complementary sites within target mRNAs, but not all of these canonical sites are equally effective, and both computational and in vivo UV-crosslinking approaches suggest that many mRNAs are targeted through non-canonical interactions. Here, we show that recently reported non-canonical sites do not mediate repression despite binding the miRNA, which indicates that the vast majority of functional sites are canonical. Accordingly, we developed an improved quantitative model of canonical targeting, using a compendium of experimental datasets that we pre-processed to minimize confounding biases. This model, which considers site type and another 14 features to predict the most effectively targeted mRNAs, performed significantly better than existing models and was as informative as the best high-throughput in vivo crosslinking approaches. It drives the latest version of TargetScan (v7.0;, thereby providing a valuable resource for placing miRNAs into gene-regulatory networks.
Full-text available
DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.
Full-text available
Background: Autism spectrum disorder (ASD) is a common neurodevelopmental disorder that lacks adequate screening tools, often delaying diagnosis and therapeutic interventions. Despite a substantial genetic component, no single gene variant accounts for >1 % of ASD incidence. Epigenetic mechanisms that include microRNAs (miRNAs) may contribute to the ASD phenotype by altering networks of neurodevelopmental genes. The extracellular availability of miRNAs allows for painless, noninvasive collection from biofluids. In this study, we investigated the potential for saliva-based miRNAs to serve as diagnostic screening tools and evaluated their potential functional importance. Methods: Salivary miRNA was purified from 24 ASD subjects and 21 age- and gender-matched control subjects. The ASD group included individuals with mild ASD (DSM-5 criteria and Autism Diagnostic Observation Schedule) and no history of neurologic disorder, pre-term birth, or known chromosomal abnormality. All subjects completed a thorough neurodevelopmental assessment with the Vineland Adaptive Behavior Scales at the time of saliva collection. A total of 246 miRNAs were detected and quantified in at least half the samples by RNA-Seq and used to perform between-group comparisons with non-parametric testing, multivariate logistic regression and classification analyses, as well as Monte-Carlo Cross-Validation (MCCV). The top miRNAs were examined for correlations with measures of adaptive behavior. Functional enrichment analysis of the highest confidence mRNA targets of the top differentially expressed miRNAs was performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID), as well as the Simons Foundation Autism Database (AutDB) of ASD candidate genes. Results: Fourteen miRNAs were differentially expressed in ASD subjects compared to controls (p <0.05; FDR <0.15) and showed more than 95 % accuracy at distinguishing subject groups in the best-fit logistic regression model. MCCV revealed an average ROC-AUC value of 0.92 across 100 simulations, further supporting the robustness of the findings. Most of the 14 miRNAs showed significant correlations with Vineland neurodevelopmental scores. Functional enrichment analysis detected significant over-representation of target gene clusters related to transcriptional activation, neuronal development, and AutDB genes. Conclusion: Measurement of salivary miRNA in this pilot study of subjects with mild ASD demonstrated differential expression of 14 miRNAs that are expressed in the developing brain, impact mRNAs related to brain development, and correlate with neurodevelopmental measures of adaptive behavior. These miRNAs have high specificity and cross-validated utility as a potential screening tool for ASD.
Full-text available
In the last 30 years, twin studies have indicated a strong genetic contribution to Autism Spectrum Disorders (ASD). The heritability of ASD is estimated to be 50 %, mostly captured by still unknown common variants. In approximately 10 % of patients with ASD, especially those with intellectual disability, de novo copy number or single nucleotide variants affecting clinically relevant genes for ASD can be identified. Given the function of these genes, it was hypothesized that abnormal synaptic plasticity and failure of neuronal/synaptic homeostasis could increase the risk of ASD. In parallel, abnormal levels of blood serotonin and melatonin were reported in a subset of patients with ASD. These biochemical imbalances could act as risk factors for the sleep/circadian disorders that are often observed in individuals with ASD. Here, we review the main pathways associated with ASD, with a focus on the roles of the synapse and the serotonin-NAS-melatonin pathway in the susceptibility of ASD.
Full-text available
MicroRNAs (miRs) are non-coding gene transcripts abundantly expressed in both the developing and adult mammalian brain. They act as important modulators of complex gene regulatory networks during neuronal development and plasticity. miR-181c is highly abundant in cerebellar cortex and its expression is increased in autism patients as well as in an animal model of autism. To systematically identify putative targets of miR-181c, we repressed this miR in growing cortical neurons and found over 70 differentially expressed target genes using transcriptome profiling. Pathway analysis showed that the miR-181c-modulated genes converge on signaling cascades relevant to neurite and synapse developmental processes. To experimentally examine the significance of these data, we inhibited miR-181c during rat cortical neuronal maturation in vitro; this loss-of miR-181c function resulted in enhanced neurite sprouting and reduced synaptogenesis. Collectively, our findings suggest that miR-181c is a modulator of gene networks associated with cortical neuronal maturation.
Full-text available
Background Autism spectrum disorders (ASD) are a group of neurodevelopmental disorders caused by the interaction between genetic vulnerability and environmental factors. MicroRNAs (miRNAs) are key posttranscriptional regulators involved in multiple aspects of brain development and function. Previous studies have investigated miRNAs expression in ASD using non-neural cells like lymphoblastoid cell lines (LCL) or postmortem tissues. However, the relevance of LCLs is questionable in the context of a neurodevelopmental disorder, and the impact of the cause of death and/or post-death handling of tissue likely contributes to the variations observed between studies on brain samples.MethodsmiRNA profiling using TLDA high-throughput real-time qPCR was performed on miRNAs extracted from olfactory mucosal stem cells (OMSCs) biopsied from eight patients and six controls. This tissue is considered as a closer tissue to neural stem cells that could be sampled in living patients and was never investigated for such a purpose before. Real-time PCR was used to validate a set of differentially expressed miRNAs, and bioinformatics analysis determined common pathways and gene targets. Luciferase assays and real-time PCR analysis were used to evaluate the effect of miRNAs misregulation on the expression and translation of several autism-related transcripts. Viral vector-mediated expression was used to evaluate the impact of miRNAs deregulation on neuronal or glial cells functions.ResultsWe identified a signature of four miRNAs (miR-146a, miR-221, miR-654-5p, and miR-656) commonly deregulated in ASD. This signature is conserved in primary skin fibroblasts and may allow discriminating between ASD and intellectual disability samples. Putative target genes of the differentially expressed miRNAs were enriched for pathways previously associated to ASD, and altered levels of neuronal transcripts targeted by miR-146a, miR-221, and miR-656 were observed in patients’ cells. In the mouse brain, miR-146a, and miR-221 display strong neuronal expression in regions important for high cognitive functions, and we demonstrated that reproducing abnormal miR-146a expression in mouse primary cell cultures leads to impaired neuronal dendritic arborization and increased astrocyte glutamate uptake capacities.Conclusions While independent replication experiments are needed to clarify whether these four miRNAS could serve as early biomarkers of ASD, these findings may have important diagnostic implications. They also provide mechanistic connection between miRNA dysregulation and ASD pathophysiology and may open up new opportunities for therapeutic.
Overview Sensory Perception Attention Memory Executive Function Cognitive Profiles Conclusion
The development of various diagnostic concepts and their continued refinement have enriched both clinical work and research. The proposal in DSM-5 for reconceptualization of a new “autism spectrum” disorder concept has some merit as well as some potential limitations. Clearly, major changes impact clinical work and research (particularly research involving longitudinal or epidemiological samples). It seems likely that we are entering a period in which a range of diagnostic approaches will be used; whether this will help or hinder research remains an important question.
Genetic variants conferring risk for autism spectrum disorder (ASD) have been identified, but the role of post-transcriptional mechanisms in ASD is not well understood. We performed genome-wide microRNA (miRNA) expression profiling in post-mortem brains from individuals with ASD and controls and identified miRNAs and co-regulated modules that were perturbed in ASD. Putative targets of these ASD-affected miRNAs were enriched for genes that have been implicated in ASD risk. We confirmed regulatory relationships between several miRNAs and their putative target mRNAs in primary human neural progenitors. These include hsa-miR-21-3p, a miRNA of unknown CNS function that is upregulated in ASD and that targets neuronal genes downregulated in ASD, and hsa_can_1002-m, a previously unknown, primate-specific miRNA that is downregulated in ASD and that regulates the epidermal growth factor receptor and fibroblast growth factor receptor signaling pathways involved in neural development and immune function. Our findings support a role for miRNA dysregulation in ASD pathophysiology and provide a rich data set and framework for future analyses of miRNAs in neuropsychiatric diseases.
Description of System: The Autism and Developmental Disabilities Monitoring (ADDM) Network is an active surveillance system in the United States that provides estimates of the prevalence of ASD and other characteristics among children aged 8 years whose parents or guardians live in 11 ADDM sites in the United States. ADDM surveillance is conducted in two phases. The first phase consists of screening and abstracting comprehensive evaluations performed by professional providers in the community. Multiple data sources for these evaluations include general pediatric health clinics and specialized programs for children with developmental disabilities. In addition, most ADDM Network sites also review and abstract records of children receiving specialeducation services in public schools. The second phase involves review of all abstracted evaluations by trained clinicians to determine ASD surveillance case status. A child meets the surveillance case definition for ASD if a comprehensive evaluation of that child completed by a qualified professional describes behaviors consistent with the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR) diagnostic criteria for any of the following conditions: autistic disorder, pervasive developmental disorder-not otherwise specified (including atypical autism), or Asperger disorder. This report provides updated prevalence estimates for ASD from the 2010 surveillance year. In addition to prevalence estimates, characteristics of the population of children with ASD are described. Results: For 2010, the overall prevalence of ASD among the ADDM sites was 14.7 per 1,000 (one in 68) children aged 8 years. Overall ASD prevalence estimates varied among sites from 5.7 to 21.9 per 1,000 children aged 8 years. ASD prevalence estimates also varied by sex and racial/ethnic group. Approximately one in 42 boys and one in 189 girls living in the ADDM Network communities were identified as having ASD. Non-Hispanic white children were approximately 30% more likely to be identified with ASD than non-Hispanic black children and were almost 50% more likely to be identified with ASD than Hispanic children. Among the seven sites with sufficient data on intellectual ability, 31% of children with ASD were classified as having IQ scores in the range of intellectual disability (IQ ≤70), 23% in the borderline range (IQ = 71-85), and 46% in the average or above average range of intellectual ability (IQ > 85). The proportion of children classified in the range of intellectual disability differed by race/ethnicity. Approximately 48% of non-Hispanic black children with ASD were classified in the range of intellectual disability compared with 38% of Hispanic children and 25% of non-Hispanic white children. The median age of earliest known ASD diagnosis was 53 months and did not differ significantly by sex or race/ethnicity. Interpretation: These findings from CDC's ADDM Network, which are based on 2010 data reported from 11 sites, provide updated population-based estimates of the prevalence of ASD in multiple communities in the United States. Because the ADDM Network sites do not provide a representative sample of the entire United States, the combined prevalence estimates presented in this report cannot be generalized to all children aged 8 years in the United States population. Consistent with previous reports from the ADDM Network, findings from the 2010 surveillance year were marked by significant variations in ASD prevalence by geographic area, sex, race/ethnicity, and level of intellectual ability. The extent to which this variation might be attributable to diagnostic practices, underrecognition of ASD symptoms in some racial/ethnic groups, socioeconomic disparities in access to services, and regional differences in clinical or school-based practices that might influence the findings in this report is unclear. Public Health Action: ADDM Network investigators will continue to monitor the prevalence of ASD in select communities, with a focus on exploring changes within these communities that might affect both the observed prevalence of ASD and population-based characteristics of children identified with ASD. Although ASD is sometimes diagnosed by 2 years of age, the median age of the first ASD diagnosis remains older than age 4 years in the ADDM Network communities. Recommendations from the ADDM Network include enhancing strategies to address the need for 1) standardized, widely adopted measures to document ASD severity and functional limitations associated with ASD diagnosis; 2) improved recognition and documentation of symptoms of ASD, particularly among both boys and girls, children without intellectual disability, and children in all racial/ethnic groups; and 3) decreasing the age when children receive their first evaluation for and a diagnosis of ASD and are enrolled in community-based support systems.